Quick Demo

Sachin Yadav , IIT Gandhinagar, sachin.yadav@iitgn.ac.in

IRIS Flower data set

The data set contains information about three species of IRIS flowers namely:

• Iris setosa
• Iris versicolor
• Iris virginica

Four features are collected from each sample, sepal-length, sepal-width, petal-length and petal-width in centi-meters.

# Common imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import plotly.express as px

iris_df = pd.read_csv("./data/IRIS.csv.gz", compression="gzip")
iris_df.sample(5)

	Unnamed: 0	sepal_length	sepal_width	petal_length	petal_width	species
118	118	7.7	2.6	6.9	2.3	Iris-virginica
9	9	4.9	3.1	1.5	0.1	Iris-setosa
137	137	6.4	3.1	5.5	1.8	Iris-virginica
130	130	7.4	2.8	6.1	1.9	Iris-virginica
48	48	5.3	3.7	1.5	0.2	Iris-setosa

fig = px.scatter_3d(iris_df, x='sepal_length', y='sepal_width', z='petal_width',
              color='species', template="plotly_dark")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

COVID-19 Dataset

covid_df = pd.read_csv("./data/owid-covid-data.csv.gz", compression="gzip")
covid_df.sample(5)

	Unnamed: 0	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
143884	143884	SWE	Europe	Sweden	2020-07-02	69297.0	689.0	755.429	5411.0	41.0	...	18.8	18.9	NaN	2.22	82.80	0.945	NaN	NaN	NaN	NaN
79806	79806	KEN	Africa	Kenya	2020-10-15	43143.0	602.0	423.571	805.0	8.0	...	1.2	20.4	24.651	1.40	66.70	0.601	NaN	NaN	NaN	NaN
155992	155992	ARE	Asia	United Arab Emirates	2021-06-27	626936.0	2122.0	2129.571	1796.0	4.0	...	1.2	37.4	NaN	1.20	77.97	0.890	NaN	NaN	NaN	NaN
112128	112128	MKD	Europe	North Macedonia	2020-09-10	15414.0	121.0	93.143	637.0	3.0	...	NaN	NaN	NaN	4.28	75.80	0.774	NaN	NaN	NaN	NaN
58224	58224	GHA	Africa	Ghana	2020-04-04	205.0	0.0	9.143	5.0	0.0	...	0.3	7.7	41.047	0.90	64.07	0.611	NaN	NaN	NaN	NaN

5 rows × 68 columns

required_columns = ["iso_code", "location", "continent", "date", "new_cases_smoothed", "total_cases"]
covid_df = covid_df.dropna(subset = required_columns)

covid_df = covid_df.sort_values("date")

covid_df[['iso_code', 'location']].sample(8)

	iso_code	location
82436	KGZ	Kyrgyzstan
57237	GEO	Georgia
65648	HTI	Haiti
134032	SGP	Singapore
125588	SHN	Saint Helena
146791	TJK	Tajikistan
103666	MOZ	Mozambique
130859	SAU	Saudi Arabia

Covid-19 New Cases Worldwide on 23 December 2021

covid_day_df = covid_df[covid_df.date == "2021-12-23"]
covid_day_df.sample(5)

	Unnamed: 0	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
104088	104088	MOZ	Africa	Mozambique	2021-12-23	167307.0	2445.0	1511.143	1961.0	1.0	...	5.1	29.1	12.227	0.70	60.85	0.456	NaN	NaN	NaN	NaN
163127	163127	WLF	Oceania	Wallis and Futuna	2021-12-23	454.0	0.0	0.000	7.0	0.0	...	NaN	NaN	NaN	NaN	79.94	NaN	NaN	NaN	NaN	NaN
73986	73986	IRL	Europe	Ireland	2021-12-23	680375.0	7411.0	5689.571	5890.0	0.0	...	23.0	25.7	NaN	2.96	82.30	0.955	NaN	NaN	NaN	NaN
2158	2158	ALB	Europe	Albania	2021-12-23	206935.0	319.0	286.714	3181.0	1.0	...	7.1	51.2	NaN	2.89	78.57	0.795	NaN	NaN	NaN	NaN
22653	22653	VGB	North America	British Virgin Islands	2021-12-23	3016.0	130.0	18.571	39.0	0.0	...	NaN	NaN	NaN	NaN	79.07	NaN	NaN	NaN	NaN	NaN

5 rows × 68 columns

fig = px.scatter_geo(covid_day_df, locations="iso_code", color="continent",
                     hover_name="location", size="new_cases_smoothed",
                     projection="natural earth", template="plotly_dark")

fig.show()

Covid-19 Total Cases over the days

fig = px.scatter_geo(covid_df, locations="iso_code", color="continent",
                     hover_name="location", size="total_cases",
                     projection="natural earth", animation_frame="date", template="plotly_dark")

fig.show()

MNIST Dataset

import torchvision
import os

import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.animation import FuncAnimation
from matplotlib import animation

rc('animation', html='jshtml')

frn = 10 # Number of frames to process in the animation
fps = 0.5 # Frames per second
mywriter = animation.PillowWriter(fps=fps) 

mnist_dataset = torchvision.datasets.MNIST(root = "data/mnist", train = True, download = True, transform=torchvision.transforms.ToTensor())

fig, ax = plt.subplots(figsize = (10, 10))

def change_plot(frame_idx):
    ax.cla()
    image_tensor = mnist_dataset[frame_idx][0]
    image_tensor_gray = image_tensor[0]
    image_tensor_gray = image_tensor_gray * 255
    ax.matshow(image_tensor_gray, cmap = "gray")
    for i in range(image_tensor_gray.shape[0]):
        for j in range(image_tensor_gray.shape[1]):
            ax.text(i, j, str(int(image_tensor_gray[j][i].item())), va = "center", ha = "center", color = "blue", fontsize = "small")
    ax.axis("off")
    plt.tight_layout()

anim = FuncAnimation(fig, change_plot, frn, interval=1000 / fps)
plt.close()
anim

Once Loop Reflect

<Figure size 432x288 with 0 Axes>

mywriter = animation.PillowWriter(fps=fps)

if not os.path.exists("./assets/gif"):
    os.makedir("./assets/gif")
    
anim.save('./assets/gif/mnist.gif',writer=mywriter)

<Figure size 432x288 with 0 Axes>